AWS Generative AI Essentials

Overview

This course introduces developers to Generative AI with AWS from Amazon Q Developer that integrates directly into your development environment to Amazon Bedrock with its foundation models and customization features. You'll learn implementation strategies for integrating these AWS AI services into your development workflow, including working with prompts, setting up guardrails, and implementing best practices for building AI applications. You'll learn about how AWS has transformed the world of sports analytics, marketing platforms, and travel services. Get started and you'll discover how organizations are using AWS' AI services to uplevel their development processes.

Syllabus

Getting Started with AI-Powered Development

This foundational module introduces AWS's core AI development tools - Amazon Q, Amazon Bedrock, and Amazon Kiro - teaching students how to effectively leverage these services for practical AI implementation. Students learn essential concepts including prompt engineering techniques like COSTAR framework, non-deterministic systems, and spec-driven development, while gaining hands-on experience with AI-assisted coding, model selection, and workflow automation. Through practical demonstrations and real-world examples, learners will master the skills needed to build AI-powered applications, optimize AI interactions for cost and performance, and integrate generative AI capabilities into their development workflow using AWS's enterprise-grade AI services.

Secure AI Application Development

This module explores essential security considerations and best practices for developing AI applications, with a focus on Amazon Bedrock Guardrails and secure coding patterns. Students learn about critical security concepts including prompt injection attacks, content filtering, PII protection, and how to implement robust safeguards using AWS tools like Bedrock Guardrails, Amazon CloudWatch monitoring, and Amazon Q Developer. Through hands-on demonstrations and practical examples, learners gain the skills to identify security vulnerabilities, implement protective measures, and develop AI applications that maintain data privacy while following industry best practices for secure AI development.

Advanced AI Development and Integration

This advanced module explores cutting-edge AI development techniques and enterprise integration patterns, focusing on Model Context Protocol (MCP) servers, Amazon Kiro's spec-driven development, and AI agent orchestration with knowledge bases. Students learn how to implement real-time data integration through MCP, structure large-scale AI projects using spec-driven methodologies, and build sophisticated AI agents capable of complex decision-making and multi-step task execution. Through hands-on exposure to production deployment strategies and knowledge base implementation, learners gain practical skills for developing scalable, enterprise-grade AI applications while mastering best practices for AI system architecture and integration.